Supernatant
data <- read_DIA_report("data/TLR4/20240828_120228_ASC_TERT_TLR_10_4_SN_Report.tsv")
data <- data[, data$Cell.Type != "ASC_TERT_TLR10_LOV"]
data_MS1.df <- assay(data, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.df <- assay(data, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data.log2 <- data
assays(data.log2) %<>% lapply(log2)
data_MS1.log2.df <- assay(data.log2, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.log2.df <- assay(data.log2, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
plot_intensity_boxplot(data_MS1.log2.df, data_MS2.log2.df)
meanSdPlot(assay(data.log2, "MS1"))
meanSdPlot(assay(data.log2, "MS2"))



data.norm <- data
assays(data.norm) %<>% lapply(normalize_matrix, "vsn")
data_MS1.norm.df <- assay(data.norm, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.norm.df <- assay(data.norm, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
plot_intensity_boxplot(data_MS1.norm.df, data_MS2.norm.df)
meanSdPlot(assay(data.norm, "MS1"))
meanSdPlot(assay(data.norm, "MS2"))



plot_pca(data.norm, scale = T, plot_all = T, maxPC = 4)



contrast_list <- c(
LightVsDark = "ASC_TERT_TLR4_LOV.Light - ASC_TERT_TLR4_LOV.Dark",
LPSVsDark = "ASC_TERT_TLR4_LOV.LPS - ASC_TERT_TLR4_LOV.Dark",
LightVsLPS = "ASC_TERT_TLR4_LOV.Light - ASC_TERT_TLR4_LOV.LPS"
)
fit <- fit_DEqMS_model(data.norm, contrast_list)
VarianceBoxplot(fit)

ASC TERT TLR4LOV Light vs. Dark
current_contrast <- 1
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.norm[rownames(data.norm) %in% res.sig$gene,
str_detect(colnames(data.norm), "Light|Dark")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
print(paste("Found", nrow(data.sig.df), "differentially expressed proteins."))
write.csv(res, file = "results/TLR4/DEA/ASC_TERT_TLR4LOV_Light_vs_Dark_SN_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/TLR4/DEA/ASC_TERT_TLR4LOV_Light_vs_Dark_SN_filtered.csv",
row.names = FALSE)
## [1] "Found 211 differentially expressed proteins."
ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)

plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ASC_TERT_TLR4_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)


plot_volcano(res, "ASC TERT TLR4LOV Light vs. Dark Supernatant", vp_lfc_limit)
## Warning: ggrepel: 180 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

ASC TERT TLR4LOV LPS vs. Dark
current_contrast <- 2
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.norm[rownames(data.norm) %in% res.sig$gene,
str_detect(colnames(data.norm), "LPS|Dark")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
print(paste("Found", nrow(data.sig.df), "differentially expressed proteins."))
write.csv(res, file = "results/TLR4/DEA/ASC_TERT_TLR4LOV_LPS_vs_Dark_SN_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/TLR4/DEA/ASC_TERT_TLR4LOV_LPS_vs_Dark_SN_filtered.csv",
row.names = FALSE)
## [1] "Found 615 differentially expressed proteins."
ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)

plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ASC_TERT_TLR4_LOV" = "lightgreen"),
Condition=c("LPS" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)


plot_volcano(res, "ASC TERT TLR4LOV LPS vs. Dark Supernatant", vp_lfc_limit)
## Warning: ggrepel: 596 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

ASC TERT TLR4LOV Light vs. LPS
current_contrast <- 3
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.norm[rownames(data.norm) %in% res.sig$gene,
str_detect(colnames(data.norm), "Light|LPS")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
print(paste("Found", nrow(data.sig.df), "differentially expressed proteins."))
write.csv(res, file = "results/TLR4/DEA/ASC_TERT_TLR4LOV_Light_vs_LPS_SN_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/TLR4/DEA/ASC_TERT_TLR4LOV_Light_vs_LPS_SN_filtered.csv",
row.names = FALSE)
## [1] "Found 597 differentially expressed proteins."
ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)

plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ASC_TERT_TLR4_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"LPS" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)


plot_volcano(res, "ASC TERT TLR4LOV Light vs. LPS Supernatant", vp_lfc_limit)
## Warning: ggrepel: 565 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Whole Cell Lysate
data <- read_DIA_report("data/TLR4/20240828_120420_ASC_TERT_TLR_10_4_WCL_Report.tsv")
data <- data[, data$Cell.Type != "ASC_TERT_TLR10_LOV"]
data_MS1.df <- assay(data, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.df <- assay(data, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data.log2 <- data
assays(data.log2) %<>% lapply(log2)
data_MS1.log2.df <- assay(data.log2, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.log2.df <- assay(data.log2, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
plot_intensity_boxplot(data_MS1.log2.df, data_MS2.log2.df)
meanSdPlot(assay(data.log2, "MS1"))
meanSdPlot(assay(data.log2, "MS2"))



data.norm <- data
assays(data.norm) %<>% lapply(normalize_matrix, "vsn")
data_MS1.norm.df <- assay(data.norm, "MS1") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
data_MS2.norm.df <- assay(data.norm, "MS2") %>%
as.data.frame() %>%
rownames_to_column("Gene.Name")
plot_intensity_boxplot(data_MS1.norm.df, data_MS2.norm.df)
meanSdPlot(assay(data.norm, "MS1"))
meanSdPlot(assay(data.norm, "MS2"))



plot_pca(data.norm, scale = T, plot_all = T, maxPC = 4)



contrast_list <- c(
LightVsDark = "ASC_TERT_TLR4_LOV.Light - ASC_TERT_TLR4_LOV.Dark",
LPSVsDark = "ASC_TERT_TLR4_LOV.LPS - ASC_TERT_TLR4_LOV.Dark",
LightVsLPS = "ASC_TERT_TLR4_LOV.Light - ASC_TERT_TLR4_LOV.LPS"
)
fit <- fit_DEqMS_model(data.norm, contrast_list)
VarianceBoxplot(fit)

ASC TERT TLR4LOV Light vs. Dark
current_contrast <- 1
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.norm[rownames(data.norm) %in% res.sig$gene,
str_detect(colnames(data.norm), "Light|Dark")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
print(paste("Found", nrow(data.sig.df), "differentially expressed proteins."))
write.csv(res, file = "results/TLR4/DEA/ASC_TERT_TLR4LOV_Light_vs_Dark_WCL_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/TLR4/DEA/ASC_TERT_TLR4LOV_Light_vs_Dark_WCL_filtered.csv",
row.names = FALSE)
## [1] "Found 252 differentially expressed proteins."
ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)

plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ASC_TERT_TLR4_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)


plot_volcano(res, "ASC TERT TLR4LOV Light vs. Dark Whole Cell Lysate", vp_lfc_limit)
## Warning: ggrepel: 228 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

ASC TERT TLR4LOV LPS vs. Dark
current_contrast <- 2
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.norm[rownames(data.norm) %in% res.sig$gene,
str_detect(colnames(data.norm), "LPS|Dark")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
print(paste("Found", nrow(data.sig.df), "differentially expressed proteins."))
write.csv(res, file = "results/TLR4/DEA/ASC_TERT_TLR4LOV_LPS_vs_Dark_WCL_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/TLR4/DEA/ASC_TERT_TLR4LOV_LPS_vs_Dark_WCL_filtered.csv",
row.names = FALSE)
## [1] "Found 318 differentially expressed proteins."
ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)

plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ASC_TERT_TLR4_LOV" = "lightgreen"),
Condition=c("LPS" = "orange",
"Dark" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)


plot_volcano(res, "ASC TERT TLR4LOV LPS vs. Dark Whole Cell Lysate", vp_lfc_limit)
## Warning: ggrepel: 286 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

ASC TERT TLR4LOV Light vs. LPS
current_contrast <- 3
res <- outputResult(fit, coef_col = current_contrast) %>%
as_tibble() %>%
mutate(qvalue = qvalue(P.Value)$qvalues,
sca.qvalue = qvalue(sca.P.Value)$qvalues) %>%
select(!c(adj.P.Val, sca.adj.pval))
res.sig <- res %>%
filter(sca.qvalue < 0.05 & abs(logFC) >= 0.58)
qvals.sig <- res.sig$sca.qvalue
names(qvals.sig) <- res.sig$gene
data.sig <- data.norm[rownames(data.norm) %in% res.sig$gene,
str_detect(colnames(data.norm), "Light|LPS")]
data.sig.df <- assay(data.sig, "MS2") %>% as.data.frame()
print(paste("Found", nrow(data.sig.df), "differentially expressed proteins."))
write.csv(res, file = "results/TLR4/DEA/ASC_TERT_TLR4LOV_Light_vs_LPS_WCL_unfiltered.csv",
row.names = FALSE)
write.csv(res.sig, file = "results/TLR4/DEA/ASC_TERT_TLR4LOV_Light_vs_LPS_WCL_filtered.csv",
row.names = FALSE)
## [1] "Found 177 differentially expressed proteins."
ggplot(data = res, aes(x = sca.P.Value)) +
geom_histogram(binwidth = 0.025)

plot_heatmap(data.sig, heatmap.colors, list(Celltype=c("ASC_TERT_TLR4_LOV" = "lightgreen"),
Condition=c("Light" = "orange",
"LPS" = "midnightblue")),
qvalues = qvals.sig, title = "Rel. LogIntensity", max_rows = hm_max_rows,
cluster_rows = hm_cluster_rows, cluster_cols = hm_cluster_cols,
scale_by_row = hm_scale_by_row)


plot_volcano(res, "ASC TERT TLR4LOV Light vs. LPS Whole Cell Lysate", vp_lfc_limit)
## Warning: ggrepel: 152 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
